Bayesian WIMP detection with the Cherenkov Telescope Array

Abstract Over the past decades Bayesian methods have become increasingly popular in astronomy and physics as stochastic samplers enabled efficient investigation of high-dimensional likelihood surfaces. In this work we develop a hierarchical inference framework to detect presence dark matter annihilation events data from Cherenkov Telescope Array (CTA). Gamma-ray are weighted based on their measured sky position Ω̂ m energy E order derive posterior distribution for matter's velocity averaged cross section 〈 σv 〉. The signal model astrophysical background cast prior distributions (Ω̂ , ). shape these can be fixed first-principle models; or one may adopt flexible priors include theoretical uncertainty, example, spectrum location. We demonstrate utility formalism using simulated with Galactic Centre scalar singlet dark-matter model. sensitivity according our method is comparable previous estimates CTA sensitivity.